Optimization of timing for data collection and analysis in advanced patient management system

ABSTRACT

Advanced patient management systems include a unit for collecting data from a device associated with a patient, and a host in communication with the unit, the host identifying a time for the device to update data on the device. The system can identify a time for the device to update data associated with the device by developing a histogram of the availability of the device for interrogation. The system can also optimize processing loads for the system by developing a histogram of the time at which the caregiver is most likely to access the system to review the collected and processed data. Methods for optimizing device data update and processing times are also included.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.11/174,922, filed Jul. 5, 2005, the contents of which are incorporatedherein by reference.

TECHNICAL FIELD

The present disclosure relates generally to advanced patient managementsystems. More particularly, the present disclosure relates to theoptimization of the timing for data collection and analysis in advancedpatient management systems.

BACKGROUND

Management of patients with chronic disease consumes a significantproportion of the total health care expenditure in the United States.Many of these diseases, such as heart disease, are widely prevalent andhave significant annual incidences as well. Patients with chronic heartdisease can receive implanted cardiac rhythm management (CRM) devicessuch as pacemakers, implantable cardioverter defibrillators (ICDs), andheart failure cardiac resynchronization therapy (CRT) devices to providetreatment for the disease.

Advanced patient management (APM) systems allow caregivers to remotelygather and analyze data associated with a patient and the patient's CRMdevice. APM systems provide a vast amount of information to thecaregiver in an automated manner. This information can provide insightsinto a patient's well being and help the caregiver predict significantchanges in a patient's health, such as a decompensation event associatedwith a heart attack. However, the time lag between when data is updatedon a CRM device and when it is collected, analyzed, and presented forreview by the APM system can reduce the timeliness of the informationprovided to the caregiver.

For example, CRM devices can update device data stored in the CRM devicememory at periodic intervals, such as once per day. One example ofdevice data that can be updated periodically by a CRM device is heartrate variability. For example, the CRM device can be programmed toupdate an average heart rate variability for a patient once per day. Thetiming for these device updates is usually arbitrarily set at the timeat which the CRM device is originally initiated prior to or at the timeof implantation. There can be a significant time lag due to a lack ofcoordination between the device data update time by a CRM device and thetime at which an APM system collects data from (e.g., interrogates) thedevice.

For example, a CRM device can be arbitrarily set to update device dataat time-of-day A in day 1, as shown in FIG. 1. Assume that the APMsystem interrogates the CRM device at time-of-day B in day 2, and thatthe caregiver accesses the APM system to review the information that theAPM system has collected from the device and analyzed at time-of-day C.Although the entire interval or lag D between device data (time-of-dayA) and caregiver review (time-of-day C) spans two days, it is arelatively short period, so that the caregiver is reviewing recentlyacquired and analyzed information.

However, in another example shown in FIG. 2, assume again that the CRMdevice is arbitrarily set to update device data at time-of-day A earlierin day 1, and that the APM system interrogates the CRM device attime-of-day B in day 2. Also assume that the caregiver does not reviewthe information on the APM system until later at time-of-day C. In thisscenario, lag D is more significant, resulting in less-timelyinformation being provided to the caregiver. In a worst-case scenariobased on daily device updates and interrogations, the caregiver could bepresented with information that is forty-eight (48) hours old. It isdesirable to minimize lag D so that the caregiver is given data that isas current as possible so that the caregiver can make timely decisionsregarding a patient's health.

In addition to the potential time lag problems associated with thecollection of data, an APM system can potentially be used to analyzedata associated with thousands or millions of patients at any giventime. It is therefore desirable to optimize analysis of data on the APMsystem such that the APM system can efficiently analyze each patient'sdata while presenting current data to each caregiver as the caregiveraccesses the APM system.

SUMMARY

The present disclosure relates generally to advanced patient managementsystems. More particularly, the present disclosure relates to theoptimization of the timing for data collection and analysis in advancedpatient management systems.

According to one aspect, an advanced patient management system includesa unit for collecting data from a device associated with a patient, anda host in communication with the unit, the host identifying a time forthe unit to collect data from the device.

According to another aspect, a method for collecting and analyzing dataassociated with a device of a patient by an advanced patient managementsystem includes: identifying a time period during which the device ismost likely to be available for data collection, setting a device updatetime based on the time period during which the device is most likely tobe available for data collection, and setting a data collection timebased on the time period during which the device is most likely to beavailable for data collection.

According to yet another aspect, a method for gathering and processingdata associated with a device of a patient by an advanced patientmanagement system includes: identifying a time period during which dataassociated with the patient is most likely to be accessed by acaregiver, setting an analysis time for the data based on the timeperiod during which the data associated with the patient is most likelyto be accessed, and analyzing the data at the analysis time.

The above summary is not intended to describe each disclosed embodimentor every implementation of the present invention. The figures and thedetailed description that follow further describe these embodiments.

DESCRIPTION OF THE DRAWINGS

Aspects of the invention may be more completely understood inconsideration of the following detailed description of variousembodiments of the invention in connection with the accompanyingdrawings, in which:

FIG. 1 illustrates an example timeline for device data update,interrogation, and review of data associated with a patient's device;

FIG. 2 illustrates another example timeline for device data update,interrogation, and review of data associated with a patient's device;

FIG. 3 illustrates an example advanced patient management system;

FIG. 4 illustrates another example timeline for device data update,interrogation, and review of data associated with a patient's device;and

FIG. 5 illustrates an example method for an advanced patient managementsystem.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

The present disclosure relates generally to advanced patient managementsystems. More particularly, the present disclosure relates to theoptimization of the timing for data collection and analysis in advancedpatient management systems.

The term “patient” is used herein to mean any individual from whominformation is collected. The term “caregiver” is used herein to meanany provider of services, such as health care providers including, butnot limited to, nurses, doctors, and other health care provider staff.

FIG. 3 illustrates an example advanced patient management system 100made in accordance with the present invention. Advanced patientmanagement (APM) system 100 generally includes the following components:a device 102, an interrogator/transceiver unit 108, a communicationsystem 200, a host 300, and a computer system 400. Each component of theAPM system 100 can communicate using the communication system 200. Somecomponents may also communicate directly with one another.

Device 102 can be an implantable device or an external device thatprovides one or more of the following functions with respect to apatient: (1) sensing, (2) data analysis, and (3) therapy. For example,in one embodiment, device 102 is either an implanted or external deviceused to measure a variety of physiological, subjective, andenvironmental conditions of a patient using electrical, mechanical,and/or chemical means. Device 102 can be configured to automaticallygather data or can require manual intervention by the patient. Device102 can be configured to store data related to the physiological and/orsubjective measurements and/or transmit the data to the communicationnetwork 200 using a variety of methods, described in detail below.Although a single device 102 is illustrated in the example embodimentshown, more devices can be used for a given patient.

In the example shown, device 102 is a cardiac rhythm management (CRM)device that is implanted within a patient. Examples of CRM devicesinclude pacemakers, cardiac resynchronization management devices,defibrillators, etc. CRM devices can have the ability to sense andcommunicate, and may also provide therapy.

In the example shown, device 102 is configured to periodically updatedata associated with the patient and/or device (collectively referred toas “device data”) and store this data in memory. These device dataupdates can, for example, be configured to occur hourly, daily, weekly,or monthly. In the examples shown, device 102 is configured to updatedevice data daily. For example, device 102 can be configured to updatephysiological measurements associated with a patient at a given timeeach day. Examples of such physiological measurements include, withoutlimitation, electrical cardiac activity (e.g., heart rate, heart ratevariability, etc.), trans-pulmonary impedance, physical motion,temperature, activity, blood pressure, breathing patterns, ejectionfractions, blood viscosity, blood chemistry, and blood glucose levels.The time-of-day at which these device data updates occur is typicallyinitially set prior to or at implantation of device 102. See, e.g.,time-of-day A shown in FIGS. 1 and 2 described above. The time-of-daycan be reset after implantation by, for example, APM system 100, asdescribed further below.

The example advanced patient management system 100 also includes one ormore interrogator/transceiver units (“ITUs”), such as example ITU 108.The ITU 108 can perform one or more of the following functions: (1) dataretrieval; (2) data storage; (3) data analysis; and (4) datacommunication. For example, the ITU 108 facilitates communicationsbetween the device 102 and the host 300 using the communication system200. The ITU 108 can, periodically or in real-time, collect and downloadinto memory (e.g., interrogate) clinically relevant patient data fromdevice 102. This data can include, in the CRM device context, forexample, P and R-wave measurements, other physiological data (e.g., HRV,activity, heart rates, etc.), pacing, shocking events, lead impedances,pacing thresholds, battery voltage, capacitor charge times, ATR episodeswith electrograms, tachycardia episodes with electrograms, histograminformation, and any other clinical information necessary to ensurepatient health and proper device function. The ITU 108 can also beconfigured to ask the patient to report symptoms or current quality oflife indications. The data is sent to the ITU 108 by the device 102 inreal-time or is periodically uploaded from buffers in the CRM device.

In the examples described herein, a single ITU 108 is described that islocated in the home of a patient. The device 102 is therefore generallyavailable for interrogation by the ITU 108 when the patient is at home.In other embodiments, multiple ITUs 108 can be placed, for example, athome and at work so that the availability for interrogation of device102 is increased. Additional details regarding an ITU, such as ITU 108,and how an ITU can function as part of an APM system, such as APM system100, can be found in U.S. patent application Ser. No. 10/330,677, filedon Dec. 27, 2002 and entitled “Advanced Patient Management SystemIncluding Interrogator/Transceiver Unit,” the entirety of which ishereby incorporated by reference.

Communication system 200 provides for communications between and amongthe various components of the APM system 100, such as the device 102,ITU 108, host 300, and computer system 400. Communications system 200can be, for example, a local area network (LAN), wide area network(WAN), or the Internet. A variety of communication methods and protocolscan be used to facilitate communication between device 102, ITU 108,communication system 200, host 300, and computer system 400. Forexample, wired and wireless communications methods can be used. Wiredcommunication methods include, for example and without limitation,traditional copper-line communications such as DSL, broadbandtechnologies such as ISDN and cable modems, and fiber optics. Wirelesscommunication methods include cellular, satellite, radio frequency (RF),Infrared, etc.

In the example embodiment illustrated, host 300 includes one or morecomputers that store patient information in one or more databases. Host300 also analyzes the data related to a patient and provides timely andpredictive assessments of the patient's well-being. For example, host300 can store historical data associated with a patient, as well as newdata that is transmitted by ITU 108 to host 300. Host 300 can analyzethis data and present the analyzed data to the caregiver in one or moreformats, as described below. For example, host 300 can compare new heartrate variability data from the CRM device 102 to historical variabilitylevels and provide the caregiver with statistical information related toany changes in heart rate variability over time.

A caregiver can access host 300 using, for example, computer system 400to review patient data that has been collected and analyzed by APMsystem 100. For example, in one embodiment, the caregiver can accessinformation on host 300 via a secure web interface over the Internet. Inanother embodiment, the data collected and analyzed by APM system 100 isdelivered to the caregiver's hospital computer system for access by thecaregiver. Other electronic delivery methods, such as email, facsimile,etc., can also be used for distribution to the caregiver.

Referring now to FIG. 4, it is desirable to minimize the interval or lagD between the time-of-day at which device data is updated by device 102(time-of-day A) and the time-of-day at which the caregiver accesses theAPM system 100 to review information associated with the patient(time-of-day C). In general, it is desirable to minimize the lag D sothat the caregiver is presented with timely information based on datathat has been recently updated, collected, and analyzed.

As noted above, the time-of-day A at which device 102 is set to updatedevice data is typically initially set prior to or during implantationof device 102 in the patient. It is desirable to set the time-of-day A(at which device data update occurs) in view of time-of-day B (at whichdevice 102 is interrogated by ITU 108 to retrieve the updated devicedata) to minimize lag D so that collected data is as timely as possible.

In the example shown in FIG. 4, APM system 100 is configured to developa time-of-day histogram X for a patient to determine when a patient'sCRM device 102 is most likely to be available for interrogation by ITU108. This histogram X can be developed over a period of time such as aweek or month. For example, during an initialization period, ITU 108 canbe configured to periodically search for device 102 at different timesof the day and record the time periods during which device 102 isavailable to ITU 108. ITU 108 and/or host 300 can then develop histogramX that represents the probability versus time-of-day that device 102 istypically available for interrogation by ITU 108.

Time-of-day A for data update on device 102 and time-of-day B forinterrogation can be set using histogram X. For example, time-of-day Bcan be set to occur when there is high or maximum probability thatdevice 120 will be available for interrogation by ITU 108. Time-of-day Afor data update on device 102 can be set just prior to the time-of-dayB. In one embodiment, time-of-day A is set to a time prior totime-of-day B at which there is a low probability (e.g., 2%) that thepatient will be available for interrogation. In another embodiment,time-of-day A is set to a pre-selected interval before time-of-day B forinterrogation.

In the examples described herein, APM system 100 can communicate withdevice 102 through ITU 108 to set time-of-day A for data update ondevice 102. In this manner, the timing for device data update can beoptimized based on the timing for interrogation.

In addition to optimizing the time-of-day A for device data update andtime-of-day B for interrogation, APM system 100 can also optimizetime-of-day C at which information related to the data collected by APMsystem 100 is available to the caregiver. For example, as shown in FIG.4, APM system 100 can develop a time-of-day histogram Y to represent theprobability versus time-of-day at which the caregiver will check thepatient's information on APM system 100. For example, during aninitialization period, APM system 100 can be configured to log times atwhich the caregiver accesses the APM system to review patientinformation.

After an initialization period, histogram Y can be created to representthe most likely times at which the caregiver will access the APM systemto review the patient's information. Histogram Y can be used to optimizewhen data collected by APM system 100 is analyzed and presented forreview by the caregiver. For example, a time-of-day E at which APMsystem 100 should complete analysis of patient data can be set justprior to the most likely time that the caregiver will access the APMsystem based on histogram Y. In one embodiment, time-of-day E is set toa time when there is a low-probability (e.g., 2%) that the caregiver hasalready accessed the APM system based on histogram Y. In an alternativeembodiment, the caregiver can manually set the time-of-day at which thecaregiver wants analyzed information to be available.

The APM system 100 can be programmed to process the device datacollected from device 102 (at time-of-day B) at any time during intervalF between time-of-days B and E to assure that the information isavailable for the caregiver at the most likely time-of-day for thecaregiver to access the APM system 100. In the examples shown, intervalF is utilized by APM system 100 to shift analysis of device data to anoptimal time during interval F so that the computational load for APMsystem 100 is balanced. In this manner, host 300 can efficiently processdata for thousands or millions of patients by distributing load over theinterval F for each patient.

In the examples shown, histograms X and Y and time-of-days B and E arecalculated using known statistical methods. For example, thetimes-of-day when the device is found to be available for interrogationcan be grouped into 15 minute bins to form the histogram X and thetimes-of-day when the caregiver accesses the APM system 100 can begrouped into 15 minute bins to form the histogram Y. Dividing each binin X or Y by the total number of entries contained in X or Y,respectively, converts X or Y into probability functions versustime-of-day. Time B can be selected to be a time when there is a high(e.g., 98%) cumulative probability that the device is available forinterrogation while time E can be selected to be a time when there is alow (e.g., 2%) cumulative probability that the caregiver have alreadyaccessed the APM system 100.

In some embodiments, time-of-day histograms are calculated for each day,week, or weekday/weekend. For example, a caregiver may have a schedulethat allows the caregiver to check patient data early in the morning onweekdays and later in the afternoon on weekends. Different time-of-dayhistograms Y can therefore be created for the given caregiver to assureoptimal data is presented to the caregiver based on the differingschedules. In addition, different delivery methods can also be used sothat, for example, the data can be available on the APM system at agiven time during the week and can be delivered wirelessly to thecaregiver's handheld device on weekends.

In yet other embodiments, the time-of-day histograms X and Y can berecalculated on a periodic basis to account for changes in a patient orcaregiver schedule. For example, if a patient switches from working aday shift to working a night shift during the week, the most likely timethat the patient's device will be available for interrogation by the ITUlocated in the patient's home is during the daytime. By developing a newtime-of-day histogram X at periodic intervals, these types of schedulechanges can be accommodated.

Referring now to FIG. 5, an example method for an APM system such assystem 100 described above is illustrated. In operation 510, the optimaltimes-of-day for device data update, interrogation, and analysis areset. As described above, for the examples herein these times are set bydeveloping one or more time-of-day histograms. For example, thetime-of-day for device data update can be set after the ITU periodicallysearches for the device to develop a time-of-day histogram to identifyat which times the device is most likely to be available forinterrogation. The optimal analysis timing can similarly be createdusing a time-of-day histogram developed by monitoring access of the APMsystem by the caregiver.

Next, in operation 520, the APM system resets the timing for device dataupdate for the device based on the time-of-day histogram. In the exampleillustrated herein, the APM system can reset the timing for device dataupdate by sending a wireless instruction through the ITU to the device.Control is then passed to operation 530, and the ITU attempts tointerrogate the device at the scheduled interrogation time-of-day.

Next, at operation 540, the data gathered by the ITU from the device isanalyzed by the host at an optimal time-of-day prior to the most likelytime at which the caregiver will access the data. Next, in operation550, the host presents the analyzed data for access by the caregiver.Control is then passed back to operation 530, and the ITU interrogatesthe device at the next time period (e.g., daily, weekly, etc.).

The systems and methods of the present disclosure can be implementedusing a system as shown in the various figures disclosed hereinincluding various devices and/or programmers, including implantable orexternal devices. Accordingly, the methods of the present disclosure canbe implemented: (1) as a sequence of computer implemented steps runningon the system; and (2) as interconnected modules within the system. Theimplementation is a matter of choice dependent on the performancerequirements of the system implementing the method of the presentdisclosure and the components selected by or utilized by the users ofthe method. Accordingly, the logical operations making up theembodiments of the method of the present disclosure described herein canbe referred to variously as operations, steps, or modules. It will berecognized by one of ordinary skill in the art that the operations,steps, and modules may be implemented in software, in firmware, inspecial purpose digital logic, analog circuits, and any combinationthereof without deviating from the spirit and scope of the presentinvention as recited within the claims attached hereto.

The present invention should not be considered limited to the particularexamples described above, but rather should be understood to cover allaspects of the invention as fairly set out in the attached claims.Various modifications, equivalent processes, as well as numerousstructures to which the present invention may be applicable will bereadily apparent to those of skill in the art to which the presentinvention is directed upon review of the instant specification.

1. A machine readable medium comprising instructions, which whenexecuted by a machine, cause the machine to: maintain a history of whena caregiver accesses a patient management system; identify a time periodduring which data associated with the device is forecasted to beaccessed by the caregiver based on the history; schedule data analysisof the data associated with the device to provide a scheduled dataanalysis time, the scheduled data analysis time based on the time periodduring which the data associated with the device is forecasted to beaccessed; and analyze the data at the scheduled data analysis time by:identifying when patient data from the device is available to thepatient management system; and scheduling the analysis time between whenpatient data is available and the time period during which dataassociated with the patient is forecasted to be accessed by thecaregiver, wherein the analysis time is scheduled at least in part tobalance computational load at the patient management system.
 2. Themachine readable medium of claim 1, wherein instructions to identify thetime period during which data associated with the device is forecastedto be accessed by the caregiver further comprise instructions to createa histogram of times during which the caregiver accesses the data on thepatient management system to determine the time period during which thedata associated with the device is forecasted to be accessed by thecaregiver.
 3. The machine readable medium of claim 2, whereininstructions to create a histogram further comprise instructions to:aggregate a plurality of instances of the time period to provide anaggregated value; and calculate the histogram using the aggregatedvalue.
 4. The machine readable medium of claim 3, wherein the pluralityof instances of the time period comprises a plurality of consecutivedays.
 5. The machine readable medium of claim 3, wherein the pluralityof instances of the time period comprises a plurality of recurringweekend days.
 6. The machine readable medium of claim 3, wherein theplurality of instances of the time period comprises a plurality ofrecurring days of a week.
 7. The machine readable medium of claim 1,wherein the instructions further comprise instructions, which whenexecuted by a machine, cause the machine to recurrently recalculate thetime period.
 8. The machine readable medium of claim 1, wherein theinstructions to identify the time period further comprise instructionsto access a time-of-day manually set by the caregiver.
 9. A method forgathering and processing data associated with a device of a patient by apatient management system, comprising: identifying, at a computersystem, a time period during which the device is forecasted to beavailable for data collection; setting a data collection time at thecomputer system, the data collection time based on the time periodduring which the device is forecasted to be available for datacollection; identifying, at the computer system, a data update time whenthe device updates data in a memory of the device, the data update timebeing before the time period during which the device is forecasted to beavailable for data collection; and configuring the device using thecomputer system, to update data at the data update time.
 10. The methodof claim 9, further comprising: maintaining a history of when a useraccesses the patient management system; identifying a time period duringwhich data associated with the device is forecasted to be accessed bythe user based on the history; scheduling data analysis of the dataassociated with the device to provide a scheduled data analysis time,the scheduled data analysis time based on a time period during which thedata associated with the device is forecasted to be accessed; andanalyzing the data at the scheduled data analysis time by: identifyingwhen patient data from the device is available to the patient managementsystem; and scheduling the analysis time between when patient data isavailable and the time period during which data associated with thepatient is forecasted to be accessed by the caregiver.
 11. The method ofclaim 10, wherein identifying the time period during which dataassociated with the device is forecasted to be accessed by the userbased on the history further comprises creating a histogram of timesduring which the user accesses data on the patient management system todetermine the time period during which the data associated with thedevice is forecasted to be accessed by the user.
 12. The method of claim11, wherein creating the histogram further comprises: determining aplurality of instances of the time period; aggregating the plurality ofinstances of the time period to provide an aggregated value; andcalculating the histogram using the aggregated value.
 13. The method ofclaim 12, wherein the plurality of instances of the time periodcomprises a recurring day of a week.
 14. The method of claim 12, whereinthe plurality of instances of the time period comprises a set of days ina week.
 15. The method of claim 10, further comprising recurrentlyrecalculating the time period during which the data associated with thedevice is forecasted to be accessed by a user.
 16. The method of claim10, wherein identifying a time period during which data associated withthe device is forecasted to be accessed by the user based on the historyfurther comprises accessing a time of day provided by the user.
 17. Amachine-readable medium comprising instructions, which when executed bya machine, cause the machine to: identify a time period during which adevice is forecasted to be available for data collection; set a datacollection time, the data collection time based on the time periodduring which the device is forecasted to be available for datacollection; identify a data update time when the device updates data ina memory of the device, the data update time being before the timeperiod during which the device is forecasted to be available for datacollection; and configure the device to update data at the data updatetime.
 18. The machine-readable medium of claim 17, wherein theinstructions further comprise instructions to: maintain a history ofwhen a user accesses the patient management system; identify a timeperiod during which data associated with the device is forecasted to beaccessed by the user based on the history; schedule data analysis of thedata associated with the device to provide a scheduled data analysistime, the scheduled data analysis time based on a time period duringwhich the data associated with the device is forecasted to be accessed;and analyze the data at the scheduled data analysis time by: identifyingwhen patient data from the device is available to the patient managementsystem; and scheduling the analysis time between when patient data isavailable and the time period during which data associated with thepatient is forecasted to be accessed by the caregiver.
 19. Themachine-readable medium of claim 18, wherein the instructions toidentify the time period during which data associated with the device isforecasted to be accessed by the user based on the history furthercomprise instructions to create a histogram of times during which theuser accesses data on the patient management system to determine thetime period during which the data associated with the device isforecasted to be accessed by the user.
 20. The machine-readable mediumof claim 19, wherein the instructions to create the histogram furthercomprise instructions to: determine a plurality of instances of the timeperiod; aggregate the plurality of instances of the time period toprovide an aggregated value; and calculate a histogram using theaggregated value.
 21. The machine-readable medium of claim 17, whereinthe instructions to identify the time period during which dataassociated with the device is forecasted to be accessed by the userbased on the history further comprise instructions to access a time ofday provided by the user.
 22. A machine-readable medium comprisinginstructions for communicating between an implanted device and a localtransceiver, which when executed by the implanted device, cause theimplanted device to: identify a data update time when the implanteddevice updates data in a memory of the implanted device, the data updatetime being before a time period during which the implanted device isforecasted to be available for data collection; and configure theimplanted device to update data at the data update time.
 23. Themachine-readable medium of claim 22, wherein the time period duringwhich the implanted device is forecasted to be available for datacollection is calculated using a histogram.